An integrated approach for optimization of solid rocket motor

An integrated approach using hyper-heuristic based on meta-heuristics is applied in optimization of solid rocket motor. We propose a non-learning random function to control low-level meta-heuristics to increase certainty of global solution. A comprehensive empirical study investigates the performance of the proposed algorithm yielding satisfactory results. Design of solid rocket motor becomes an exigent task when accounting for chamber design, nozzle design, ballistic performance calculations as well as grain geometry and regression. CAD modeling overcomes the limitation posed by analytical expression thus increased model fidelity. CAD model allows different sub-systems to be modeled separately that not only prevents feature creation failures but also allows ease in modification of the model. Motor performance is calculated using a simplified ballistic model. Mass is the impetus driver on performance, and so considered as core of solid rocket motor design process. Therefore, we intend to minimize gross mass through hyper-heuristic approach. The approach produced satisfactory results for test case.